A mixed graph model for community detection

Anita Keszler, T. Szirányi

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

A mixed graph theoretic model is proposed for finding communities in a social network. Information on the habits (shopping habits, free time activities) is considered to be known at least for part of the society. The presented model is based on applying parallelly a standard and a bipartite graph. Compared to previous methods, the introduced algorithm has the advantage of noise-tolerance and is suitable independently of the size of the clusters in the graph. Clusters in the dataset tend to form dense subgraphs in both graph models. The idea is to speed up cluster core mining by a modified MST algorithm. Noise in the dataset is defined as missing information on a person's habits. Clustering noisy data is done by using a bipartite graph and fuzzy membership functions. The proposed algorithm can be used for predicting the missing data estimated on the available information patterns. The presented mixed graph model might also be used for image processing tasks.

Original languageEnglish
Pages (from-to)479-494
Number of pages16
JournalInternational Journal of Intelligent Information and Database Systems
Volume6
Issue number5
DOIs
Publication statusPublished - Sep 2012

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Membership functions
Image processing

Keywords

  • Clustering
  • Community detection
  • Dense subgraph mining
  • Incomplete data
  • Noise tolerance
  • Social networks

ASJC Scopus subject areas

  • Information Systems

Cite this

A mixed graph model for community detection. / Keszler, Anita; Szirányi, T.

In: International Journal of Intelligent Information and Database Systems, Vol. 6, No. 5, 09.2012, p. 479-494.

Research output: Contribution to journalArticle

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